KR20230048421A - LIDAR window blocking detection - Google Patents

Abstract

Systems, methods and computer-readable media for detecting LIDAR window obstacles in an FMCW LIDAR system, analyzing operational effects of LIDAR window obstacles on an FMCW LIDAR system, and mitigating operational effects on an FMCW LIDAR system. Methods that can be implemented are disclosed.

Description

LIDAR window blocking detection

[0001] This application claims the 35 U.S.C. of U.S. Patent Application Serial No. 16/994,109, filed on August 14, 2020. § 119(e), the entire contents of this application are hereby incorporated herein by reference.

[0002] This disclosure relates generally to light detection and ranging (LIDAR) systems, and more specifically to the detection, assessment, and mitigation of obstacles on a viewing window of a LIDAR system.

[0003] A Frequency-Modulated Continuous-Wave (FMCW) LIDAR system has tunable lasers for frequency-chirped illumination of targets, and a signal from the targets that is coupled with a local copy of the transmitted signal. Use coherent receivers for detection of backscattered or reflected light. Mixing the local copy with the delayed return signal by the round-trip time to and from the target produces a beat frequency at the receiver that is proportional to the distance of each target from the system's field of view.

[0004] These systems may be used on autonomous vehicles for navigation where the external environment may be harsh. To protect the optical, electro-optical and mechanical components of the systems, the light beam is transmitted through sealed windows. However, the beams may be blocked by rain, water droplets, mud, road salt, insects or other debris, which may block or dampen the beams and pose a safety hazard.

[0005] This disclosure describes various examples of LIDAR systems and methods for detecting, evaluating, and mitigating the effects of obstacles on LIDAR windows.

[0006] In one example, a LIDAR system according to the present disclosure includes an optical scanner that sends a light beam through a LIDAR window and receives a return signal from reflections of the light beam. The LIDAR system also includes an optical processing system that generates a range-dependent baseband signal corresponding to the return signal. The LIDAR system also includes a signal processing system comprising a processor and a non-transitory memory, which, when executed by the processor, causes the LIDAR system to determine whether a return signal is caused by an obstacle on the LIDAR window, and to the LIDAR system. It stores instructions that allow determining the motional effects of an obstacle on the motion and mitigating the motional effects of an obstacle.

[0007] In one example, to determine whether a return signal is caused by an obstacle on a LIDAR window, the signal processing system is configured to detect frequencies of the range-dependent baseband signal that are below a threshold frequency.

[0008] In one example, to determine operational effects of an obstacle, a LIDAR system is instructed to generate a reflectivity map to identify an obstructed field of view (FOV) and energy reflected by the obstacle.

[0009] In one example, the LIDAR system is instructed to analyze a reflectivity map of the FOV to determine whether the blocked FOV is a safe threshold FOV and to determine whether the maximum detection range is less than the minimum safe threshold detection range.

[0010] In one example, the LIDAR system is a vehicle-mounted system, and the signal processing system is configured to mitigate operational effects of an obstacle by one or more of slowing the vehicle, parking the vehicle, and cleaning the LIDAR window. do.

[0011] In one example, an optical processing system includes a light source to generate an FMCW light beam, an optical coupler to receive the FMCW light beam from the light source, and an optical coupler to direct the FMCW light beam to an optical scanner and receive a return signal from the optical scanner. A polarization beam splitter (PBS) coupled with, and a photodetector (PD) receiving a return signal from the PBS and a sample of the FMCW light beam from the optical coupler - the PD receives the return signal and the FMCW light beam configured to generate a range-dependent baseband signal from spatial mixing of samples of

[0012] In one example, a signal processing system includes a sampler coupled with an optical processing system to generate time domain samples of a range-dependent baseband signal, a discrete coupled sampler to convert the time domain samples to the frequency domain. A discrete Fourier transform (DFT) processor, and a peak search processor coupled to the DFT processor to search for energy peaks at frequencies below a threshold frequency.

[0013] In one example, the signal processing system also includes a frequency compensation processor coupled to a peak search engine to correct for Doppler scanning artifacts, and a frequency compensation processor to generate a reflectivity map and a LIDAR window health report. Contains a linked post-processor.

[0014] In one example, a method in a LIDAR system includes detecting an obstacle on or near a LIDAR window from a LIDAR return signal; Determining the operational effect of the LIDAR window obstacle on the LIDAR system and mitigating the operational effect of the LIDAR window obstacle on the LIDAR system.

[0015] In one example, the non-transitory computer-readable medium includes instructions for a processor of a LIDAR system, the instructions causing the LIDAR system to detect an obstacle on or near the LIDAR window from the LIDAR return signal. to determine the operational effect of LIDAR window obstacles on the LIDAR system, and to mitigate the operational effect of LIDAR window obstacles on the LIDAR system.

[0016] For a more complete understanding of the various examples, reference is now made to the detailed description below taken in conjunction with the accompanying drawings in which like identifiers correspond to like elements.
[0001] FIG. 1 is a block diagram illustrating an exemplary LIDAR system according to the present disclosure.
[0017] FIG. 2 is a time-frequency diagram illustrating an example of LIDAR waveforms according to the present disclosure.
[0018] Figure 3 is a block diagram illustrating an exemplary light processing system in accordance with the present invention.
[0019] FIG. 4 is a time-scale plot illustrating an example of a time-domain range-dependent baseband signal according to the present disclosure.
5 is a block diagram illustrating an exemplary signal processing system according to the present disclosure.
[0021] FIG. 6 is an illustration of an example of a LIDAR window reflectivity map according to this disclosure.
[0022] FIG. 7 is an example of sampling timelines illustrating a tradeoff between angular resolution and frequency resolution according to the present disclosure.
[0023] FIG. 8 is a flow diagram illustrating an example method for detecting and evaluating LIDAR window blocking according to the present disclosure.
9 is a block diagram illustrating a system for detecting and evaluating LIDAR window blocking according to the present disclosure.

[0025] This disclosure describes various examples of LIDAR systems and methods for detecting and mitigating the effects of obstacles on LIDAR windows. According to some embodiments, the described LIDAR system may be implemented in any sensing market, such as but not limited to transportation, manufacturing, metrology, medical and security systems. According to some embodiments, the described LIDAR system is implemented as part of the front-end of a frequency modulated continuous-wave (FMCW) device that supports spatial awareness for autonomous driver assistance systems or self-driving vehicles. do.

1 illustrates a LIDAR system 100 according to example implementations of the present disclosure. The LIDAR system 100 includes one or more of each of multiple components, but may include fewer or additional components than shown in FIG. 1 . As shown, the LIDAR system 100 includes optical circuits 101 implemented on a photonics chip. Optical circuits 101 may include a combination of active optical components and passive optical components. Active optical components may generate, amplify and/or detect optical signals and the like. In some examples, an active optical component includes light beams at different wavelengths and includes one or more optical amplifiers, one or more optical detectors, and the like.

[0027] The free space optics 115 may include one or more optical waveguides to carry the optical signal and route and manipulate the optical signal to appropriate input/output ports of the active optical circuit. Free space optics 115 may also include taps, wavelength division multiplexers (WDM), splitters/combiners, polarization beam splitters (PBS), collimators. )s, couplers, and the like. In some examples, free space optics 115 may include components that convert the polarization state and direct the received polarized light to a light detector using, for example, a PBS. Free space optics 115 may further include a diffractive element that deflects light beams with different frequencies at different angles along an axis (eg, fast-axis).

[0028] In some examples, the LIDAR system 100 has an axis orthogonal or substantially orthogonal to the fast-axis of the diffractive element to steer the light signals to scan the environment according to the scanning pattern (eg, and an optical scanner 102 comprising one or more scanning mirrors rotatable along a slow-axis. For example, the scanning mirrors may be rotatable by means of one or more galvanometers. The optical scanner 102 also collects light incident on any objects in the environment into a returning light beam that is returned to the passive optical circuit component of the optical circuits 101 . For example, the returning light beam may be directed to the photo detector by a polarizing beam splitter. In addition to mirrors and galvanometers, optical scanner 102 may include components such as quarter wave plates, lenses, anti-reflective coated windows, and the like.

[0029] To control and support the optical circuits 101 and the optical scanner 102, the LIDAR system 100 includes LIDAR control systems 110. LIDAR control systems 110 may include a processing device for LIDAR system 100 . In some examples, the processing device may be one or more general purpose processing devices such as a microprocessor, central processing unit, or the like. More specifically, the processing device is a complex instruction set computing (CISC) microprocessor, reduced instruction set computer (RISC) microprocessor, very long instruction word (VLIW) microprocessor. It may be a processor, or processors that implement other instruction sets, or processors that implement a combination of instruction sets. A processing device may also include one or more special purpose processing devices such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), network processors, and the like. can be picked up

In some examples, LIDAR control systems 110 may include a signal processing unit 112 such as a DSP. The LIDAR control systems 110 are configured to output digital control signals to control the light driver 103 . In some examples, digital control signals may be converted to analog signals via signal conversion unit 106 . For example, the signal conversion unit 106 may include a digital-to-analog converter. The light drivers 103 may then provide drive signals to the active light components of the light circuits 101 to drive light sources such as lasers and amplifiers. In some examples, several light drivers 103 and signal conversion units 106 may be provided to drive a plurality of light sources.

[0031] The LIDAR control system 110 is also configured to output digital control signals for the light scanner 102. Motion control system 105 may control the galvanometers of optical scanner 102 based on control signals received from LIDAR control systems 110 . For example, a digital-to-analog converter may convert coordinate routing information from LIDAR control systems 110 into signals interpretable by the galvanometers of light scanner 102 . In some examples, motion control system 105 may also return information about the position or motion of components of light scanner 102 to LIDAR control systems 110 . For example, the analog-to-digital converter may in turn convert information about the position of the galvanometer into a signal interpretable by the LIDAR control systems 110 .

[0032] The LIDAR control systems 110 are further configured to analyze incoming digital signals. In this regard, LIDAR system 100 includes optical receivers 104 for measuring one or more beams received by optical circuit 101 . For example, a reference beam receiver may measure the amplitude of a reference beam from an active optical component, and an analog-to-digital converter converts signals from the reference receiver into signals interpretable by the LIDAR control systems 110. convert to Target receivers measure an optical signal that conveys information about the target's range and speed in the form of a beat frequency, modulated optical signal. The reflected beam may be mixed with a second signal from the local oscillator. Optical receivers 104 may include high-speed analog-to-digital converters to convert signals from the target receiver into signals interpretable by LIDAR control systems 110 . In some examples, signals from optical receivers 104 may be subjected to signal conditioning by signal conditioning unit 107 prior to reception by LIDAR control systems 110 . For example, signals from optical receivers 104 may be provided to an operational amplifier for amplification of the received signals and the amplified signals may be provided to LIDAR control systems 110 .

[0033] In some applications, the LIDAR system 100 adds one or more imaging devices 108 configured to capture images of the environment, a global positioning system 109 configured to provide a geographic location of the system, or other sensor inputs. can be included with LIDAR system 100 may also include image processing system 114 . Image processing system 114 may be configured to receive images and geographic location and transmit images and location or information related thereto to LIDAR control systems 110 or other systems coupled to LIDAR system 100 .

[0034] In operation according to some examples, the LIDAR system 100 is configured to use nondegenerate light sources to simultaneously measure range and velocity across two dimensions. This capability allows real-time, long-range measurements of range, speed, azimuth and elevation of the surrounding environment.

In some examples, the scanning process begins with light drivers 103 and LIDAR control systems 110 . The LIDAR control systems 110 instruct the light drivers 103 to independently modulate one or more light beams, and these modulated signals propagate through the passive light circuit to the collimator. The collimator directs light in a light scanning system that scans the environment through a pre-programmed pattern defined by motion control system 105 . The optical circuits 101 may also include a polarization wave plate (PWP) to convert the polarization of the light as it leaves the optical circuits 101 . In some examples, the polarizing wave plate may be a quarter wave plate or a half wave plate. A portion of the polarized light may also be reflected back to the optical circuits 101 . For example, lensing or collimation systems used in LIDAR system 100 may have natural reflective properties or a reflective coating to reflect some of the light back into optical circuits 101 .

[0036] The optical signals reflected back from the environment are transmitted to the receivers through the optical circuits 101. Since the light's polarization has been converted, it can be reflected by the polarizing beam splitter along with a portion of the polarized light that is reflected back into the optical circuits 101 . Thus, rather than returning to the same fiber or waveguide as the light source, the reflected light is reflected to separate light receivers. These signals interfere with each other to create a combined signal. Each beam signal returning from the target creates a time-shifted waveform. The temporal phase difference between the two waveforms produces the measured beat frequency at optical receivers (photodetectors). The combined signal may then be reflected back to optical receivers 104 .

[0037] Analog signals from optical receivers 104 are converted to digital signals using ADCs. The digital signals are then transmitted to the LIDAR control systems 110. Signal processing unit 112 may then receive and interpret the digital signals. In some embodiments, signal processing unit 112 also receives image data from image processing system 114 as well as position data from motion control system 105 and galvanometers (not shown). Then, as the light scanner 102 scans additional points, the signal processing unit 112 may create a 3D point cloud with information about the range and speed of points in the environment. The signal processing unit 112 may also superimpose the 3D point cloud data with the image data to determine the speed and distance of objects in the surrounding area. The system also processes satellite-based navigation location data to provide an accurate global location.

로 라벨링된 스캐닝 파형(201)은 처프(chirp) 대역폭

및 처프 주기

를 갖는 톱니 파형(톱니 "처프")이다. 톱니의 기울기는

로 주어진다. 도 2는 또한 일부 실시예들에 따른 타깃 복귀 신호(202)를 묘사한다.

로 라벨링된 타깃 복귀 신호(202)는 스캐닝 신호(201)의 시간-지연된 버전이며, 여기서

는 스캐닝 신호(201)에 의해 조명된 타깃으로 그리고 타깃으로부터의 왕복 시간이다. 왕복 시간은

로서 주어지고, 여기서 R은 타깃 레인지이고,

는 광속 c인 광 빔의 속도이다. 따라서, 타깃 레인지 R은

로 계산될 수 있다. 복귀 신호(202)가 스캐닝 신호와 광학적으로 혼합될 때, 레인지 의존 차이 주파수("비트 주파수")

가 생성된다. 비트 주파수

는 톱니 기울기 k에 의해 시간 지연

와 선형적으로 관련된다. 즉,

이다. 타깃 레인지 R은

에 비례하므로, 타깃 레인지 R은

로 계산될 수 있다. 즉, 레인지 R은 비트 주파수

에 선형적으로 관련된다. 비트 주파수

는 예를 들어, 시스템(100)의 광 수신기들(104)에서의 아날로그 신호로서 생성될 수 있다. 그 후 비트 주파수는 예를 들어, LIDAR 시스템(100)의 신호 컨디셔닝 유닛(107)과 같은 신호 컨디셔닝 유닛에서 아날로그-대-디지털 변환기(ADC: analog-to-digital converter)에 의해 디지털화될 수 있다. 그 후 디지털화된 비트 주파수 신호는 예를 들어, 시스템(100)의 신호 프로세싱 유닛(112)과 같은 신호 프로세싱 유닛에서 디지털로 프로세싱될 수 있다. 타깃 복귀 신호(202)는 일반적으로 또한 타깃이 LIDAR 시스템(100)에 대한 속도를 갖는 경우 주파수 오프셋(도플러 시프트)을 포함할 것이라는 점에 유의해야 한다. 도플러 시프트는 별도로 결정될 수 있으며 복귀 신호의 주파수를 보정하는 데 사용될 수 있어, 도플러 시프트는 설명의 단순함 및 용이성을 위해 도 2에 도시되지 않는다. 또한 ADC의 샘플링 주파수는 에일리어싱(aliasing) 없이 시스템에 의해 프로세싱될 수 있는 가장 높은 비트 주파수를 결정할 것이라는 점에 유의해야 한다. 일반적으로, 프로세싱될 수 있는 최고 주파수는 샘플링 주파수의 절반(즉, "나이퀴스트(Nyquist) 한계")이다. 일 예에서, 제한 없이, ADC의 샘플링 주파수가 1 기가헤르츠인 경우, 에일리어싱 없이 프로세싱될 수 있는 최고 비트 주파수

는 500 메가헤르츠이다. 이러한 한계는 결국 처프 기울기 k를 변경하여 조정될 수 있는

로서 시스템의 최대 레인지를 결정한다. 일 예에서, ADC로부터의 데이터 샘플들은 연속적일 수 있지만, 아래에 설명되는 후속 디지털 프로세싱은 LIDAR 시스템(100)의 일부 주기성과 연관될 수 있는 "시간 세그먼트들"로 분할될 수 있다. 일 예에서, 제한 없이, 시간 세그먼트는 사전 결정된 수의 처프 주기 T 또는 광 스캐너에 의한 방위각의 전체 회전 수에 대응할 수 있다.2 is a time-frequency diagram 200 of an FMCW scanning signal 201 that may be used by a LIDAR system, such as system 100, to scan a target environment in accordance with some embodiments. In one example,

The scanning waveform 201 labeled as chirp bandwidth

and chirp period

is a sawtooth waveform (sawtooth “chirp”) with the inclination of the teeth

is given as 2 also depicts a target return signal 202 in accordance with some embodiments.

The target return signal 202, labeled as , is a time-delayed version of the scanning signal 201, where

is the round trip time to and from the target illuminated by the scanning signal 201. round trip time

is given as, where R is the target range,

is the speed of the light beam with the speed of light c. Therefore, the target range R is

can be calculated as When the return signal 202 is optically mixed with the scanning signal, the range dependent difference frequency ("beat frequency")

is created beat frequency

is the time delay by the sawtooth slope k

is linearly related to in other words,

am. The target range R is

Since it is proportional to , the target range R is

can be calculated as That is, the range R is the beat frequency

is linearly related to beat frequency

may be generated as an analog signal at optical receivers 104 of system 100, for example. The beat frequency can then be digitized by an analog-to-digital converter (ADC) in a signal conditioning unit, such as, for example, signal conditioning unit 107 of LIDAR system 100. The digitized beat frequency signal may then be digitally processed in a signal processing unit, such as, for example, signal processing unit 112 of system 100. It should be noted that the target return signal 202 will typically also include a frequency offset (Doppler shift) if the target has velocity relative to the LIDAR system 100. The Doppler shift can be determined separately and used to correct the frequency of the return signal, so the Doppler shift is not shown in FIG. 2 for simplicity and ease of explanation. It should also be noted that the ADC's sampling frequency will determine the highest bit frequency that can be processed by the system without aliasing. In general, the highest frequency that can be processed is half the sampling frequency (ie, the "Nyquist limit"). In one example, without limitation, if the sampling frequency of the ADC is 1 gigahertz, the highest bit frequency that can be processed without aliasing

is 500 MHz. These limits can eventually be tuned by changing the chirp slope k.

Determines the maximum range of the system as In one example, the data samples from the ADC may be continuous, but the subsequent digital processing described below may be divided into "time segments" that may be associated with some periodicity of the LIDAR system 100. In one example, without limitation, a time segment may correspond to a predetermined number of chirp periods T or a total number of rotations in azimuth by the optical scanner.

3 is a block diagram illustrating an example optical system 300 in accordance with some embodiments. The optical system 300 may include an optical scanner 301 similar to the optical scanner 102 illustrated and described with respect to FIG. 1 . The optical system 300 may also include a light processing system 302 , which may include, for example, free space optics 115 , light circuits 101 , light drivers 103 , light receivers 104 . ) and elements of the signal conversion unit 106.

The light processing system 302 can include a light source 303 for generating a frequency modulated continuous wave (FMCW) light beam 304 . The light beam 304 is configured to couple the light beam 304 to a polarizing beam splitter (PBS) 306 and to couple a sample 307 of the light beam 304 to a photo detector (PD) 308. It may be directed to coupler 305 . The PBS 306 is configured to direct the light beam 304 to the light scanner 301 due to the polarization of the light beam 304 . The light scanner 301 is configured to scan the target environment with a light beam 304 through a range of azimuth and elevation angles covering the field of view (FOV) of the LIDAR window 309 . In FIG. 3, for ease of illustration, only the azimuth scan is illustrated.

[0041] As shown in FIG. 3, at one azimuthal angle (or range of angles), the light beam 304 may pass through the LIDAR window 309 and illuminate the target 310 without being blocked. The return signal 311-1 from the target 310 will pass unblocked through the LIDAR window 309 and will be directed back to the PBS 306 by the optical scanner 301. At a later time in the scan (ie, increased azimuth), the light beam 304 may be directed by the light scanner 301 to a location on the LIDAR window 309 that is blocked or partially blocked by the obstacle 312. . As a result, the light beam 304 will pass through the LIDAR window 309 and be reflected or partially reflected by the obstacle 312 . Return signal 311-2 from obstacle 312 will pass back through LIDAR window 309 and will be directed by light scanner 301 back to PBS 306. Also illustrated in FIG. 3 is an attenuated light beam 304A representing the attenuated portion of the light beam 304 that is not reflected or absorbed by the obstruction 312 . For simplicity, the attenuated light beam 304A is not shown illuminating another target.

)이다.[0042] Combined return signal 311 (return signal 311-1 from target 310 and Time domain signals including return signal 311 - 2 from obstacle 312 ) are directed by PBS 306 to photodetector (PD) 308 . At PD 308, the combined return signal 311 is spatially mixed with local samples 307 of light beam 304 to generate a range-dependent baseband signal 313 in the time domain. The range-dependent baseband signal 313 is the frequency difference between the combined return signal 311 and the local samples of 307 over time (i.e.,

)am.

[0043] FIG. 4 is a time-magnitude plot of an example of a time-domain range-dependent baseband signal 313 generated by an embodiment of the present disclosure. In FIG. 4, the horizontal axis may represent time or an azimuth scan angle as a function of time. For a given scanning direction illustrated and described with respect to FIG. 3 , light beam 304 is first reflected by target 310 and then by obstacle 312 as illustrated in FIG. 4 . Although the magnitude of the signal due to the obstacle 312 in FIG. 4 is shown to be greater than the signal due to the target 310, it should be noted that this may not always be the case depending on the range of the target and the reflection coefficient of the obstacle 312. do. For example, as described below in FIG. 5, the signal processing systems described by embodiments of the present disclosure process the information depicted in FIG. 4 to generate discrete time-domain sequences that can be used for further processing. do.

이고, 여기서

은 비트 주파수이고,

는 왕복 이동 시간이고, k는 도 2와 관련하여 상술한 바와 같이 처프 파형의 기울기이다.5 is a block diagram illustrating an exemplary signal processing system 500 according to embodiments of the present disclosure. Signal processing system 500 includes, without limitation, signal conversion unit 106, signal conditioning unit 107, LIDAR control systems 110, signal processing unit 112 and motion control system 105 as shown in FIG. It may include all or part of one or more components described above in relation to it. Each of the functional blocks of the signal processing system 500 may be realized as hardware firmware, software or some combination of hardware, firmware and software. In FIG. 5 , the range-dependent baseband signal 313 generated from the optical processing system 300 is a time-domain sampler 501 that converts the continuous range-dependent baseband signal 313 into a discrete time-domain sequence 502. ) is provided. The discrete time sequence 502 is provided to a discrete Fourier transform (DFT) processor 503 which transforms the discrete time domain sequence 502 into a discrete frequency domain sequence 504 . In the discrete frequency domain sequence 504, the frequencies associated with the obstacle 312 are associated with the target 310 because the round-trip time to and from the obstacle 312 is less than the round-trip time to and from the target 310. It will be appreciated that the frequency of a beat in a baseband signal that is lower than those frequencies, range-dependent, is proportional to the range. in other words,

and here

is the beat frequency,

is the round-trip travel time, and k is the slope of the chirp waveform as described above with reference to FIG. 2 .

[0045] Since the geometry of the LIDAR system, including the distance of the LIDAR window from the light source, is known, any beat frequency below a given threshold frequency, such as threshold frequency 505 in FIG. 5, can be determined to be associated with a LIDAR window obstacle. there is. The discrete frequency domain sequence 504 searches and identifies energy peaks in the frequency domain for target returns (e.g., target returns 311-1) and returns from LIDAR window obstacles (e.g., obstacles ( Returns 311-2 from 312) are provided to the peak search processor 506 that identifies all of them. In one example, the signal processing system 500 also includes a frequency compensation processor 507 to correct and/or remove frequency artifacts introduced by the system. For example, in some scenarios, the scanning process itself may introduce Doppler frequency shifts due to high speed rotation of the mirrors in the optical scanner 301 . After frequency compensation by the frequency compensation processor 507, the information provided by the peak search processor 506, including the energy-frequency profile of the return signal reflected from the range-dependent baseband signal 313, is stored in the post-processor 508 ) is provided. Post-processor 508 uses the processing instructions stored in memory 509 to obtain the return signal's energy-frequency profile and azimuth and elevation data from motion control system 105, e.g., from LIDAR system 100. Combined to generate a reflectivity map of the LIDAR window's field of view (FOV) and a full window health report.

6 is an example of a reflectivity map 600 . The exemplary reflectivity map 600 includes a plot of adjacent obstacles 601 . Each point on obstacle 601 represents a pixel in the FOV of a LIDAR window, such as LIDAR window 309 of light processing system 300 of FIG. 3 , where each pixel is associated with an azimuth and an elevation angle. In the example of FIG. 6 , the azimuth spans 100 degrees from -50 degrees to +50 degrees, and the elevation angle spans 60 degrees from -30 degrees to +30 degrees. The reflectivity of the LIDAR window can be plotted as a reflectivity contour at each elevation angle and each azimuth angle. For example, in FIG. 6 , reflectivity versus azimuth at elevation of 20 degrees is illustrated by reflectivity contour 602 . Similarly, reflectivity versus elevation at an azimuth angle of -30 degrees is illustrated by reflectivity contour 603 .

[0047] The reflectivity map and window health report are used by signal processing unit 112 and LIDAR control systems 110 to determine any operational effects of an obstacle or obstacles and to take any actions necessary to mitigate the operational effects. can be triggered. According to some embodiments, the window health report includes details as to which portion of the FOV of window 309 is blocked and to what extent. For example, a window health report may include information relating to whether or not the LIDAR system 100 has sufficient visibility to operate safely. According to some embodiments, if the window health report indicates an unsafe operating condition due to the level of window obstruction, the LIDAR control system 110 allows the vehicle or other platform carrying the LIDAR system 100 to take corrective action. You may be directed to park in a safe location. The reflectivity map and window health report can be used to map blocked or damaged views and calculate maximum detection ranges at damaged FOVs based on the amount of energy reflected from the obstacle(s) and the signal-to-noise ratios of the return signal. Signal processing unit 112 may use this information to determine if the blocked FOV is a safety critical FOV, such as, for example, a forward view of a moving vehicle. Signal processing system 500 may also determine if the reduction in maximum detection range is a safety threshold impairment that prevents detection and avoidance of roadway obstacles or other moving vehicles beyond the safety threshold minimum detection range. In one example involving a vehicle-mounted LIDAR system, signal processing unit 112 and LIDAR control system 110 may take corrective action to mitigate operating effects. These corrective actions include, for example, control signals that cause the vehicle to slow down to increase reaction time, park in a safe location, and/or implement a window cleaning procedure such as activating a window washing, window wiper system. It may be to send them.

[0048] According to some embodiments, the LIDAR systems described herein may vary the sampling rate for use by the time domain sampler 501 to sample the range-dependent baseband signal 313 and the per pixel of the LIDAR window field of view. may consist of a number of samples. As shown by timelines 701 and 702 in FIG. 7, the number of samples per pixel is assumed to have a fixed sampling rate. Timeline 701 illustrates N samples per pixel while timeline 702 illustrates 2N samples per pixel. The choice of the number of samples per pixel presents a tradeoff between angular resolution (i.e. precision of the position of obstacles) and frequency resolution (which translates to range resolution). When the number of samples per pixel is reduced, the angular resolution may increase and the range resolution may decrease. In another scenario, increasing the number of samples per pixel may decrease the angular resolution and increase the range resolution. According to some embodiments, time domain sampler 501 may increase the sampling rate when window occlusion is detected to better analyze the portion of the occluded FOV. An increase in sampling rate can be used to increase angular resolution without sacrificing range resolution.

[0049] FIG. 8 is a flow diagram illustrating an example method 800 for detecting and mitigating operational effects of LIDAR window blocking in accordance with this disclosure. Method 800 begins at operation 802 of generating a range-dependent baseband signal (eg, baseband signal 313) from an FMCW LIDAR return signal (eg, return signal 311). Method 800 continues at operation 804 of sampling the range-dependent baseband signal in the time domain (e.g., with time domain sampler 501): The method 800 continues at operation 806 of transforming the time domain samples to the frequency domain (e.g., with the DFT processor 503). Method 800 continues at operation 808 of searching (eg, with peak search processor 506) for frequency domain energy peaks at frequencies below a threshold frequency. Method 800 continues at operation 810 of determining a blocked FOV of the FMCW LIDAR system (e.g., at post-processor 508). The method 800 continues at operation 812 of determining the reflected energy at the occluded FOV (eg, from the reflectivity map generated by the post-processor 508). Method 800 continues at operation 814 of determining whether the blocked FOV is a safe threshold FOV (eg, by signal processing unit 112 ). The method 800 continues at operation 816 where it determines (eg, by the signal processing unit 112) whether the maximum detection range is less than the minimum safe threshold detection range. The method 800 ends at operation 818 of relieving the blockage (eg, by automatically washing/wiping the windows or automatically directing the LIDAR system's host vehicle to a safe location for subsequent cleaning).

9 is a block diagram of a system 900 for detecting and mitigating operational effects of LIDAR window blocking for an FMCW LIDAR system in accordance with the present disclosure. System 900 includes signal processing unit 112 and/or processor 901 , which may be part of signal processing system 500 . System 900 also includes memory 902 (e.g., a non-transitory computer-readable medium such as ROM, RAM, flash memory, etc.) containing instructions, which instructions when executed by processing device 901 , causes the LIDAR system to perform operations, including a method for detecting and evaluating operational effects of LIDAR window obstacles on LIDAR system 100 as described with respect to FIG. In particular, non-transitory computer-readable memory 902 includes instructions 904 for generating a range-dependent baseband signal from an FMCW LIDAR return signal (eg, return signal 311 ); instructions 906 for sampling a range-dependent baseband signal in the time domain (eg, with time domain sampler 501); instructions 908 for transforming time-domain samples to the frequency domain (e.g., with DFT processor 503); instructions 910 to search for frequency domain energy peaks at frequencies below a threshold frequency (eg, with peak search processor 506); instructions 912 to determine an obstructed field of view (FOV) of the LIDAR system (eg, in post-processor 508); instructions 914 to determine reflected energy at the occluded FOV (eg, from a reflectivity map generated by post-processor 508); instructions 916 to determine whether the blocked FOV is a safety threshold FOV (eg, by signal processing unit 112); instructions 918 to determine whether a maximum detection range is less than a minimum safe threshold detection range (eg, by signal processing unit 112); and instructions 920 for relieving blockages (eg, by automatically cleaning/wiping windows or automatically directing the LIDAR system's host vehicle to a safe location for subsequent cleaning).

[0051] The above description sets forth numerous specific details such as examples of specific systems, components, methods, etc., in order to provide a thorough understanding of some examples of the present disclosure. However, it will be apparent to those skilled in the art that at least some examples of the present disclosure may be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in simplified block diagram form in order to avoid unnecessarily obscuring the present disclosure. Accordingly, the specific details presented are exemplary only. Specific examples may differ from these illustrative details and still be considered within the scope of this disclosure.

[0052] Any reference to "an example" or "an example" throughout this specification means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example. Thus, the appearances of the phrases “in one example” or “in an example” in various places throughout this specification are not necessarily all referring to the same example.

[0053] Although operations of methods are shown and described herein in a particular order, the order of operations of each method may be modified such that certain acts may be performed in reverse order or certain acts may be performed at least partially concurrently with other acts. Instructions or sub-operations of individual operations may be performed intermittently or in an alternating manner.

[0054] The foregoing description of illustrated implementations of the invention, including that described in the Abstract, is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Specific implementations and examples of the invention have been described herein for purposes of illustration, and various equivalent modifications are possible within the scope of the invention as those skilled in the relevant art will recognize. The words "example" or "exemplary" are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the words “example” or “exemplary” is intended to present concepts in a concrete manner. The term "or" as used in this application is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from context, "X includes A or B" is intended to mean any of the natural inclusive permutations. That is, X includes A; X comprises B; or when X includes both A and B, “X includes A or B” satisfies any of the above cases. Also, as used in this application and the appended claims, the singular forms "a" and "an" are generally to be construed to mean "one or more" unless the context clearly dictates otherwise. In addition, terms such as "first", "second", "third", and "fourth" used herein are intended as labels for distinguishing between different elements, and must be assigned ordinal numbers according to the corresponding number. It doesn't have meaning.

Claims (20)

As a light detection and ranging (LIDAR) system,
an optical scanner that transmits a light beam through a LIDAR window and receives a return signal from reflections of the light beam;
an optical processing system that generates a range-dependent baseband signal corresponding to the return signal; and
A signal processing system comprising:
processor;
a memory, wherein the memory, when executed by the processor, causes the LIDAR system to:
determine whether the return signal is caused by an obstacle on or near the LIDAR window;
determine operational effects of the obstacle on the LIDAR system; and
and stores instructions to mitigate the operating effects of the obstacle. According to claim 1,
To determine whether the return signal is caused by an obstacle on the LIDAR window, the processor further causes the LIDAR system to detect frequencies of the range-dependent baseband signal that are below a threshold frequency. According to claim 2,
To determine the operational effects of the obstacle, the processor further causes the LIDAR system to generate a reflectivity map to identify energy reflected by the obstacle and an obstructed field of view (FOV). LIDAR system. According to claim 3,
wherein the signal processing system further analyzes the reflectivity map of the FOV to determine whether the blocked FOV is a safety threshold FOV and to determine whether a maximum detection range is less than a minimum safety threshold detection range. According to claim 4,
The LIDAR system includes a vehicle-mounted system, the signal processing system mitigating the operational effects of the obstacle by one or more commands to slow the vehicle, park the vehicle, and clean the LIDAR window. which, the LIDAR system. According to claim 1,
The optical processing system,
a light source generating an FMCW light beam;
an optical coupler receiving the FMCW light beam from the light source;
a polarization beam splitter (PBS) coupled with the optical coupler for directing the FMCW optical beam to the optical scanner and receiving the return signal from the optical scanner; and
A photodetector (PD) receiving the return signal from the PBS and a sample of the FMCW light beam from the optical coupler - the PD receives the return signal from spatial mixing of the FMCW light beam with the sample generating a range-dependent baseband signal. According to claim 2,
The signal processing system,
a sampler coupled with the optical processing system to generate time domain samples of the range-dependent baseband signal;
a discrete Fourier transform (DFT) processor coupled to the sampler to transform the time domain samples to the frequency domain; and
and a peak search processor coupled to the DFT processor to search for energy peaks at frequencies below the threshold frequency. According to claim 6,
The signal processing system,
a frequency compensation processor coupled to the peak search engine to correct Doppler scanning artifacts; and
and a post-processor coupled to the frequency compensation processor to generate the FOV reflectivity map and LIDAR window health report. As a method in a light detection and ranging (LIDAR) system,
detecting an obstacle on or near the LIDAR window from the LIDAR return signal;
determining the operational effect of a window obstacle on the LIDAR system; and
mitigating the operating effect of the window obstacle on the LIDAR system. According to claim 9,
The step of detecting the window obstacle,
generating a range-dependent baseband signal from the LIDAR return signal; and
detecting frequencies in the range-dependent baseband signal that are less than a threshold frequency corresponding to ranges in or near the LIDAR window. According to claim 10,
Detecting frequencies below the threshold frequency in the range-dependent baseband signal comprises:
sampling the range-dependent baseband signal in the time domain;
converting time domain samples to frequency domain; and
Searching for frequency domain energy peaks at frequencies below the threshold frequency. According to claim 9,
Determining the operating effect of the window obstacle comprises:
generating a reflectivity map of a field of view (FOV) of the LIDAR system; and
analyzing the reflectivity map by identifying an occluded FOV of the LIDAR system and determining energy reflected from the occluded FOV. According to claim 12,
determining whether the blocked FOV is a safety threshold FOV; and
and determining whether the maximum detection range is less than the minimum safety threshold detection range. According to claim 9,
The LIDAR system comprises a vehicle-mounted LIDAR system, and wherein mitigating the operating effect of the window obstruction comprises at least one of slowing the vehicle, parking the vehicle, and cleaning the LIDAR window. How to. A non-transitory computer-readable storage medium containing instructions,
The instructions, when executed by the LIDAR system's processor, cause the LIDAR system to:
detect an obstacle on or near the LIDAR window from the LIDAR return signal;
determine an operating effect of the LIDAR window obstacle on the LIDAR system; and
mitigating the operating effect of the LIDAR window obstacle on the LIDAR system. According to claim 15,
To detect the LIDAR window obstacle, the LIDAR system further comprises:
generate a range-dependent baseband signal from the LIDAR return signal; and
detect frequencies in the range-dependent baseband signal that are less than a threshold frequency corresponding to ranges in or near the LIDAR window. According to claim 16,
To detect frequencies in the range-dependent baseband signal that are below the threshold frequency, the LIDAR system further comprises:
sampling the range-dependent baseband signal in the time domain;
transform time domain samples to frequency domain; and
Search for frequency domain energy peaks at frequencies below the threshold frequency. According to claim 15,
To determine the operating effect of the LIDAR window obstacle on the LIDAR system, the LIDAR system further comprises:
generate a reflectivity map of a field of view (FOV) of the LIDAR system; and
computer-readable storage memory for determining an obstructed FOV of the LIDAR system and analyzing the reflectivity map to determine energy reflected from the obstructed FOV. According to claim 18,
The LIDAR system further,
determine whether the blocked FOV is a safety threshold FOV; and
A computer-readable storage memory for determining whether a maximum detection range is less than a minimum safety threshold detection range. According to claim 15,
The LIDAR system includes a vehicle-mounted system, and to mitigate the operating effects of the LIDAR window obstruction, the LIDAR system provides instructions to slow the vehicle, park the vehicle, or clean the LIDAR window. A computer-readable storage memory that receives instructions.

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